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      <h5 class="header center orange-text">CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement</h5>
      <h6 class="header center"><i>CVPR 2020</i></h6>
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        <h5 class="header col s12 light">Ho Kei Cheng*, Jihoon Chung*, Yu-Wing Tai, Chi-Keung Tang</h5>
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          <a href="https://arxiv.org/abs/2005.02551" target="_blank"
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          <a href="https://github.com/hkchengrex/CascadePSP" target="_blank"
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          <a href="https://github.com/hkchengrex/CascadePSP/tree/master/segmentation-refinement" target="_blank"
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      <h5 class="header center">Abstract</h1>
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              State-of-the-art semantic segmentation methods were almost exclusively trained on images within a fixed resolution range. These segmentations are inaccurate for very high-resolution images since using bicubic upsampling of low-resolution segmentation does not adequately capture high-resolution details along object boundaries. In this paper, we propose a novel approach to address the high-resolution segmentation problem without using any high-resolution training data. The key insight is our CascadePSP network which refines and corrects local boundaries whenever possible. Although our network is trained with low-resolution segmentation data, our method is applicable to any resolution even for very high-resolution images larger than 4K. We present quantitative and qualitative studies on different datasets to show that CascadePSP can reveal pixel-accurate segmentation boundaries using our novel refinement module without any finetuning. Thus, our method can be regarded as class-agnostic. Finally, we demonstrate the application of our model to scene parsing in multi-class segmentation.
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    <h5 class="header center">Citation</h1>
    
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@inproceedings{CascadePSP2020,
  title={{CascadePSP}: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement},
  author={Cheng, Ho Kei and Chung, Jihoon and Tai, Yu-Wing and Tang, Chi-Keung},
  booktitle={CVPR},
  year={2020}
}</pre>
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            Please Email Ho Kei Cheng (<a href = "mailto: hkchengrex@gmail.com" style="color:#a6d9fc;">hkchengrex@gmail.com</a>) for any questions.
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